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on Financial Markets |
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Issue of 2023‒09‒04
fourteen papers chosen by |
| By: | Richard Mawulawoea Ahadzie; Dan Daugaard; Moses Kangogo; Faisal Khan; Joaquin Vespignani |
| Abstract: | This study investigates the impact of COVID-19 infections and mobility restriction policies on stock market volatility. We estimate panel data models for seven countries using daily data from February 12, 2020 to April 14, 2021. Our results show that the number of new cases of COVID-19 infections and the introduction of mobility restriction policies plays a crucial role in shaping stock market volatility during the pandemic. We found that new cases of COVID-19 infections and mobility restrictions policies increase stock market jumps, rather than increase continuous volatility. We also find that mobility restriction policies lessen the impact of new COVID-19 cases on stock market volatility. |
| Keywords: | Stock Market Volatility, New Cases of COVID-19 Infections, Mobility Restriction Policies |
| JEL: | G10 G11 G12 |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2023-40&r=fmk |
| By: | Oleksandr Romanko; Akhilesh Narayan; Roy H. Kwon |
| Abstract: | In this paper, we explore potential uses of generative AI models, such as ChatGPT, for investment portfolio selection. Trusting investment advice from Generative Pre-Trained Transformer (GPT) models is a challenge due to model "hallucinations", necessitating careful verification and validation of the output. Therefore, we take an alternative approach. We use ChatGPT to obtain a universe of stocks from S&P500 market index that are potentially attractive for investing. Subsequently, we compared various portfolio optimization strategies that utilized this AI-generated trading universe, evaluating those against quantitative portfolio optimization models as well as comparing to some of the popular investment funds. Our findings indicate that ChatGPT is effective in stock selection but may not perform as well in assigning optimal weights to stocks within the portfolio. But when stocks selection by ChatGPT is combined with established portfolio optimization models, we achieve even better results. By blending strengths of AI-generated stock selection with advanced quantitative optimization techniques, we observed the potential for more robust and favorable investment outcomes, suggesting a hybrid approach for more effective and reliable investment decision-making in the future. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.06260&r=fmk |
| By: | Kris J. Mitchener; Angela Vossmeyer; Kris James Mitchener |
| Abstract: | We examine how financial crises redistribute risk, employing novel empirical methods and micro data from the largest financial crisis of the 20th century – the Great Depression. Using balance-sheet and systemic risk measures at the bank level, we build an econometric model with incidental truncation that jointly considers bank survival, the type of bank closure (consolidations, absorption, and failures), and changes to bank risk. Despite roughly 9, 000 bank closures, risk did not leave the financial system; instead, it increased. We show that risk was redistributed to banks that were healthier prior to the financial crisis. A key mechanism driving the redistribution of risk was bank acquisition. Each acquisition increases the balance-sheet and systemic risk of the acquiring bank by 25%. Our findings suggest that financial crises do not quickly purge risk from the system, and that merger policies commonly used to deal with troubled financial institutions during crises have important implications for systemic risk. |
| Keywords: | Bayesian inference, financial crises, sample selection, mergers, banking networks |
| JEL: | G21 C30 N12 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10597&r=fmk |
| By: | Martin He{\ss}ler; Tobias Wand; Oliver Kamps |
| Abstract: | Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understand the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis which solves significant memory and computing time limitations to extract crisis information from a correlation metric. Therefore, we focus on the recently investigated S&P500 mean market correlation in a period of roughly 20 years that includes the dot-com bubble, the global financial crisis and the Euro crisis. The analysis is performed two-fold: first, in retrospect on the whole dataset and second, in an on-line adaptive manner in pre-crisis segments. The on-line sensitivity horizon is roughly determined to be 80 up to 100 trading days after a crisis onset. A detailed comparison to global economic events supports the interpretation of the mean market correlation as an informative macroeconomic measure by a rather good agreement of change point distributions and major crisis events. Furthermore, the results hint to the importance of the U.S. housing bubble as trigger of the global financial crisis, provide new evidence for the general reasoning of locally (meta)stable economic states and could work as a comparative impact rating of specific economic events. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.00087&r=fmk |
| By: | Liping Wang; Jiawei Li; Lifan Zhao; Zhizhuo Kou; Xiaohan Wang; Xinyi Zhu; Hao Wang; Yanyan Shen; Lei Chen |
| Abstract: | Predicting stock prices presents a challenging research problem due to the inherent volatility and non-linear nature of the stock market. In recent years, knowledge-enhanced stock price prediction methods have shown groundbreaking results by utilizing external knowledge to understand the stock market. Despite the importance of these methods, there is a scarcity of scholarly works that systematically synthesize previous studies from the perspective of external knowledge types. Specifically, the external knowledge can be modeled in different data structures, which we group into non-graph-based formats and graph-based formats: 1) non-graph-based knowledge captures contextual information and multimedia descriptions specifically associated with an individual stock; 2) graph-based knowledge captures interconnected and interdependent information in the stock market. This survey paper aims to provide a systematic and comprehensive description of methods for acquiring external knowledge from various unstructured data sources and then incorporating it into stock price prediction models. We also explore fusion methods for combining external knowledge with historical price features. Moreover, this paper includes a compilation of relevant datasets and delves into potential future research directions in this domain. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.04947&r=fmk |
| By: | Kaldorf, Matthias; Röttger, Joost |
| Abstract: | How does convenience yield interact with sovereign risk and the supply of government bonds? We propose a model of sovereign debt and default in which convenience yield arises because investors are able to pledge government bonds as collateral on financial markets. Convenience yield is dependent on the valuation of collateral, which is negatively dependent on the supply of government bonds, and haircuts that increase with sovereign risk. Calibrated to Italian data, convenience yield contributes substantially to the public debt-to-GDP ratio and can rationalise prolonged periods of negative bond spreads, even in the presence of default risk. We show that the debt elasticity of convenience yield is the most important driver of our results. Decomposing it into the debt elasticity of a collateral valuation and a haircut component, we find that, under empirically relevant conditions, a higher debt elasticity of haircuts can reduce fiscal discipline. |
| Keywords: | Sovereign risk, convenience yield, haircuts, debt management |
| JEL: | G12 G15 H63 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:152023&r=fmk |
| By: | Eduardo C. Garrido-Merch\'an; Sol Mora-Figueroa-Cruz-Guzm\'an; Mar\'ia Coronado-Vaca |
| Abstract: | This paper investigates the application of Deep Reinforcement Learning (DRL) for Environment, Social, and Governance (ESG) financial portfolio management, with a specific focus on the potential benefits of ESG score-based market regulation. We leveraged an Advantage Actor-Critic (A2C) agent and conducted our experiments using environments encoded within the OpenAI Gym, adapted from the FinRL platform. The study includes a comparative analysis of DRL agent performance under standard Dow Jones Industrial Average (DJIA) market conditions and a scenario where returns are regulated in line with company ESG scores. In the ESG-regulated market, grants were proportionally allotted to portfolios based on their returns and ESG scores, while taxes were assigned to portfolios below the mean ESG score of the index. The results intriguingly reveal that the DRL agent within the ESG-regulated market outperforms the standard DJIA market setup. Furthermore, we considered the inclusion of ESG variables in the agent state space, and compared this with scenarios where such data were excluded. This comparison adds to the understanding of the role of ESG factors in portfolio management decision-making. We also analyze the behaviour of the DRL agent in IBEX 35 and NASDAQ-100 indexes. Both the A2C and Proximal Policy Optimization (PPO) algorithms were applied to these additional markets, providing a broader perspective on the generalization of our findings. This work contributes to the evolving field of ESG investing, suggesting that market regulation based on ESG scoring can potentially improve DRL-based portfolio management, with significant implications for sustainable investing strategies. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.09631&r=fmk |
| By: | Bührle, Anna Theresa; Yen, Chia-Yi |
| Abstract: | When faced with higher managerial taxes, mutual fund managers who personally invest in the funds they manage take on greater risk. By exploiting the enactment of the American Taxpayer Relief Act 2012 as an exogenous tax shock, we observe that co-investing fund managers increase risk-taking by 8%. Specifically, these managers adjust their portfolios by investing in stocks with higher beta. The observed effect appears to be driven by agency incentives, particularly for funds with a more convex flow-performance relationship and for managers who have underperformed compared to their peers in the past two years. Such tax-induced behavior is associated with negative fund performance. We highlight the role of co-investment in transmitting managerial tax shocks to mutual funds. |
| Keywords: | risk-taking, taxation, mutual funds, co-investment |
| JEL: | G11 G18 G23 H24 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:23028&r=fmk |
| By: | Daher, Wassim; Karam, Fida; Ahmed, Naveed |
| Abstract: | We study a generalization of the Kyle (1985) static model with two risk neutral insiders to the case where each insider is partially informed about the value of the stock and compete under Stackelberg setting. First, we characterize the linear Bayesian equilibrium. Then, we carry out a comparative statics analysis. Our findings reveal that partial information increases the insiders profits in a Stackelberg setting than in a Cournot setting. Finally we study the impact of the information sharing on equilibrium outcomes. |
| Keywords: | Insider trading, Risk neutrality, Partial Information, Stackelberg structure, Kyle model |
| JEL: | D82 G14 |
| Date: | 2023–06–29 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:118138&r=fmk |
| By: | Matteo Prata; Giuseppe Masi; Leonardo Berti; Viviana Arrigoni; Andrea Coletta; Irene Cannistraci; Svitlana Vyetrenko; Paola Velardi; Novella Bartolini |
| Abstract: | The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL models focusing on Stock Price Trend Prediction (SPTP) based on Limit Order Book (LOB) data. To carry out this study, we developed LOBCAST, an open-source framework that incorporates data preprocessing, DL model training, evaluation and profit analysis. Our extensive experiments reveal that all models exhibit a significant performance drop when exposed to new data, thereby raising questions about their real-world market applicability. Our work serves as a benchmark, illuminating the potential and the limitations of current approaches and providing insight for innovative solutions. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.01915&r=fmk |
| By: | Höck, André; Bauckloh, Michael Tobias; Dumrose, Maurice; Klein, Christian |
| Abstract: | Demand for sustainable fixed-income investment solutions is surging but there is hardly research on the impact of sustainability on the risk characteristics of fixed-income portfolios. This study examines the impact of sustainability on the credit risk exposure of U.S. corporate bond portfolios between 2013 and 2020 by analyzing the returns of sustainable and non-sustainable portfolios using two different asset pricing models and environmental, social, and governance (ESG) ratings from different providers. Controlling for a set of portfolio characteristics, our results show that sustainable portfolios are significantly less exposed to credit risk than their non-sustainable peer portfolios. This finding implies that considering ESG criteria in portfolio management is a suitable means to systematically manage credit risk. Being the first study to investigate the relationship between sustainability and credit risk on portfolio level, this study contributes to the understanding of the effects of ESG criteria in portfolio management and provides academics and investment professionals with valuable insights. |
| Keywords: | Sustainability, Credit risk management, Corporate bonds |
| JEL: | G12 G32 Q56 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:2303&r=fmk |
| By: | Darsh Kachhara; John K. E Markin; Astha Singh |
| Abstract: | Earnings announcements (EADs) are corporate events that provide investors with fundamentally important information. The prospect of stock price rises may also contribute to EADs increased volatility. Using data on extremely short term options, we study that bimodality in the risk neutral distribution and concavity in the IV smiles are ubiquitous characteristics before an earnings announcement day. This study compares the returns between concave and non concave IV smiles to see if the concavity in the IV curve leads to any information about the risk in the market and showcases how investors hedge against extreme volatility during earnings announcements. In fact, our paper shows in the presence of concave IV smiles; investors pay a significant premium to hedge against the uncertainty caused by the forthcoming announcement. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.15718&r=fmk |
| By: | Mitsuru Katagiri; Junnosuke Shino; Koji Takahashi |
| Abstract: | This study investigates the effects of the Bank of Japan's (BOJ) exchange-traded fund (ETF) purchase program on stock returns, particularly focusing on the role of the stock lending market. Using firm-level panel data, we find that the BOJ's purchases raised stock returns more for those stocks with limited availability in the stock lending market. Nonetheless, over the longer term, the BOJ's accumulated purchases lowered lending fees and weakened the effects of their purchases on stock returns. This result suggests that ETF managers supply stocks that constitute ETFs held by the BOJ to the stock lending market, which weakens the policy effects of the program. |
| Keywords: | large-scale asset purchase (LSAP), ETF purchase program, stock lending market, Bank of Japan |
| JEL: | E58 G12 G14 |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1113&r=fmk |
| By: | Georgios Bampinas (Panteion University of Social and Political Sciences); Theodore Panagiotidis (UoM - University of Macedonia [Thessaloniki]); Panagiotis Politsidis (Audencia Business School) |
| Abstract: | We examine the asymmetric and nonlinear nature of the cross-and intra-market linkages of eleven EMU sovereign bond and CDS markets during 2006-2018. By adopting the excess correlation concept of Bekaert et al. (2005) and the local Gaussian correlation approach of Tjøstheim and Hufthammer (2013), we find that contagion phenomena occurred during two major phases. The first, extends from late 2009 to mid 2011 and concerns the outright contagion transmission from EMU South bond markets towards all European CDS markets. The second, is during the revived fears of a Greek exit in November 2011 and is characterized by contagion from (i) CDS spreads in the EMU South towards bond yields in the same bloc and Belgium, and (ii) from Italian and Spanish CDS spreads towards all European CDS spreads. Consistent with their "too big to bail out" status, Italy and Spain emerge as pivotal for the evolution of sovereign credit risk across the Eurozone. Our examination of the relevant mechanisms, highlights the importance of credit risk over liquidity risk, and the containment effect of the naked CDS ban. |
| Keywords: | sovereign bond market, sovereign CDS market, nonlinear dependence, contagion, local Gaussian correlation JEL Classification: G01 G14 G15 C1 C58, local Gaussian correlation JEL Classification: G01, G14, G15, C1, C58 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04164277&r=fmk |